With digital image processing and digital communication becoming increasingly prevalent today, increasing amounts of printed or other textual documents are being scanned for subsequent computerized processing and/or digital transmission. This processing may involve optical character recognition for converting printed characters, whether machine printed or handwritten, from scanned bit-mapped form into an appropriate character set, such as ASCII.
Gray-scale images are converted to binary images through a thresholding process. In essence, each multi-bit pixel value in a scanned gray-scale image is compared to a pre-defined threshold value, which may be fixed, variable, or adaptively variable, to yield a single corresponding output bit. If the multi-bit pixel value equals or exceeds the threshold value for that particular pixel, the resultant single-bit output pixel is set to a "ONE"; otherwise if the threshold is greater than the multi-bit pixel, then the resultant single-bit output pixel remains at "ZERO". In this manner, thresholding extracts those pixels which form desired objects from the background, with the pixels that form objects being one value, typically that for black, and the pixels for the background being another value, typically that for white. For ease of reference, we refer to each character, text, line art, or other desired object in the image as simply an "object".
There is an increasing number of business documents and forms which contain uniform color and/or gray-shaded background for enhancing presentation of the information in a document. The uniform color or gray shade in a document is often made by screened halftone dots. The percentage of halftone dots and the frequency of the halftone screen in the field determine the degree of shade. A light-shaded area contains less halftone dots, and a heavy-shaded area contains a higher percentage of halftone dots. Often, document objects are printed inside the halftoned areas.
When a document is scanned and binarized using existing thresholding techniques, the halftone dot structures in gray-shaded regions are present in the binarized image because existing document thresholding techniques are contrast-based in nature. The high occurrence of binary halftone dots in a binarized image adversely impacts image compression efficiency using standard image compression techniques. In order to reduce compressed image file size of a document image containing uniform halftone field, the removal of halftone dots is necessary before effecting image compression.
Halftone dot removal converts printed (say, black) pixels corresponding to halftone dots into un-printed (say, white) pixels (white background). Conventional halftone dot removal techniques include a process for electronically blurring the image such that halftoned regions exhibit a lesser optical density than solid regions. Next, the image is passed through a density thresholding process to remove pixels that occur in regions that have optical densities less than the threshold value. Such halftone dot removal techniques are generally effective, but tend to blur the information image.
There were several published methods for classifying halftone dots in a scanned document image for use in intelligent image rendering for scan-print systems. These methods include classifying halftone dots by examining the repetitive and periodic occurrence of halftone dots using autocorrelation (see U.S. Pat. No. 4,194,221 which issued to Stoffel on Mar. 18, 1980), feature matching with trained halftone data (see U.S. Pat. No. 4,403,257 which issued to Hsieh on Sep. 6, 1983), or frequencies of edge transition (see U.S. Pat. No. 4,722,008 which issued to Ibaraki et al. on Jan. 26, 1988). The classified halftone dots are then processed by a region growing method, such as known connected component or runlength smearing methods, to form halftone lines and regions.